chore: import upstream snapshot with attribution

test/prim/process/CMakeLists.txt

@@ -0,0 +1,10 @@
+file(
+  GLOB TEST_OPS
+  RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
+  "test_*.py")
+
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+foreach(TEST_OP ${TEST_OPS})
+  py_test_modules(${TEST_OP} MODULES ${TEST_OP} ENVS ${GC_ENVS})
+endforeach()

test/prim/process/test_prim_amp.py

@@ -0,0 +1,115 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+import paddle.nn.functional as F
+from paddle import nn
+from paddle.base import core, framework
+from paddle.nn import BatchNorm
+
+np.random.seed(2023)
+
+
+class PrimeNet(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv2D(2, 4, (3, 3), bias_attr=False)
+        self.bn = BatchNorm(4, act="relu")
+
+    def forward(self, x):
+        y = self.conv(x)
+        out = self.bn(y)
+        res = F.max_pool2d(out, kernel_size=2, stride=2, padding=0)
+        return res
+
+
+class TestPrimAMPO1(unittest.TestCase):
+    """
+    Test PrimeNet with @to_static + prim v.s Dygraph in AMPO1.
+    """
+
+    def setUp(self):
+        paddle.seed(2022)
+        self.x = paddle.randn([4, 2, 6, 6], dtype="float32")
+        self.x.stop_gradient = False
+        self.atol = 1e-3
+        self.rtol = 1e-3
+        if paddle.is_compiled_with_xpu():
+            self.atol = 5e-3
+            self.rtol = 5e-3
+
+    def train(self, use_prim):
+        core._set_prim_all_enabled(use_prim)
+        paddle.seed(2022)
+        net = PrimeNet()
+        sgd = paddle.optimizer.SGD(
+            learning_rate=0.1, parameters=net.parameters()
+        )
+
+        if use_prim:
+            net = paddle.jit.to_static(
+                net, build_strategy=False, full_graph=True
+            )
+        with paddle.amp.auto_cast(level='O1'):
+            out = net(self.x)
+            loss = paddle.mean(out)
+            loss.backward()
+            sgd.step()
+            sgd.clear_grad()
+            return loss
+
+    def test_amp_01(self):
+        if not isinstance(framework._current_expected_place(), core.CPUPlace):
+            expected = self.train(False)
+            actual = self.train(True)
+            np.testing.assert_allclose(
+                expected,
+                actual,
+                rtol=self.rtol,
+                atol=self.atol,
+            )
+
+    def test_amp_O1_infer(self):
+        if not isinstance(framework._current_expected_place(), core.CPUPlace):
+            net = PrimeNet()
+            core._set_prim_all_enabled(False)
+            net.eval()
+            static_net = paddle.jit.to_static(
+                net, build_strategy=False, full_graph=True
+            )
+            res = static_net(self.x)
+
+            # set prim all enabled
+            core._set_prim_all_enabled(True)
+            net.eval()
+            static_net = paddle.jit.to_static(
+                net, build_strategy=False, full_graph=True
+            )
+            with paddle.amp.auto_cast(level='O1'):
+                res_amp = static_net(self.x)
+
+            np.testing.assert_allclose(
+                res,
+                res_amp,
+                rtol=self.rtol,
+                atol=self.atol,
+            )
+
+
+if __name__ == '__main__':
+    unittest.main()